Parametric Trajectory Representations for Behaviour Classification

This paper presents an empirical comparison of strategies for representing motion trajectories with fixed-length vectors. We compare four techniques, which have all previously been adopted in the trajectory classification literature: least-squares cubic spline approximation, the Discrete Fourier Transform, Chebyshev polynomial approximation, and the Haar wavelet transform. We measure the class separability of five different trajectory datasets- ranging from vehicle trajectories to pen trajectories- when described in terms of these representations. Results obtained over a range of dimensionalities indicate that the different representations yield similar levels of class separability, with marginal improvements provided by Chebyshev and Spline representations. For the datasets considered here, each representation appears to yield better results when used in conjunction with a curve parametrisation strategy based on arc-length, rather than time. However, we illustrate a situation- pertinent to surveillance applications- where the converse is true

Incremental semi-supervised learning for anomalous trajectory detection

The acquisition of a scene-specific normal behaviour model underlies many existing approaches to the problem of automated video surveillance. Since it is unrealistic to acquire a comprehensive set of labelled behaviours for every surveyed scenario, modelling normal behaviour typically corresponds to modelling the distribution of a large collection of unlabelled examples. In general, however, it would be desirable to be able to filter an unlabelled dataset to remove potentially anomalous examples. This thesis proposes a simple semi-supervised learning framework that could allow a human operator to efficiently filter the examples used to construct a normal behaviour model by providing occasional feedback: Specifically, the classification output of the model under construction is used to filter the incoming sequence of unlabelled examples so that human approval is requested before incorporating any example classified as anomalous, while all other examples are automatically used for training. A key component of the proposed framework is an incremental one-class learning algorithm which can be trained on a sequence of normal examples while allowing new examples to be classified at any stage during training. The proposed algorithm represents an initial set of training examples with a kernel density estimate, before using merging operations to incrementally construct a Gaussian mixture model while minimising an information-theoretic cost function. This algorithm is shown to outperform an existing state-of-the-art approach without requiring off-line model selection. Throughout this thesis behaviours are considered in terms of whole motion trajectories: in order to apply the proposed algorithm, trajectories must be encoded with fixed length vectors. To determine an appropriate encoding strategy, an empirical comparison is conducted to determine the relative class-separability afforded by several different trajectory representations for a range of datasets. The results obtained suggest that the choice of representation makes a small but consistent difference to class separability, indicating that cubic B-Spline control points (fitted using least-squares regression) provide a good choice for use in subsequent experiments. The proposed semi-supervised learning framework is tested on three different real trajectory datasets. In all cases the rate of human intervention requests drops steadily, reaching a usefully low level of 1% in one case. A further experiment indicates that once a sufficient number of interventions has been provided, a high level of classification performance can be achieved even if subsequent requests are ignored. The automatic incorporation of unlabelled data is shown to improve classification performance in all cases, while a high level of classification performance is maintained even when unlabelled data containing a high proportion of anomalous examples is presented

Temporal dissociation of phencyclidine: Induced locomotor and social alterations in rats using an automated homecage monitoring system – implications for the 3Rs and preclinical drug discovery

Background: Rodent behavioural assays are widely used to delineate the mechanisms of psychiatric disorders and predict the efficacy of drug candidates. Conventional behavioural paradigms are restricted to short time windows and involve transferring animals from the homecage to unfamiliar apparatus which induces stress. Additionally, factors including environmental perturbations, handling and the presence of an experimenter can impact behaviour and confound data interpretation. To improve welfare and reproducibility these issues must be resolved. Automated homecage monitoring offers a more ethologically relevant approach with reduced experimenter bias. Aim: To evaluate the effectiveness of an automated homecage system at detecting locomotor and social alterations induced by phencyclidine (PCP) in group-housed rats. PCP is an NMDA receptor antagonist commonly utilised to model aspects of schizophrenia. Methods: Rats housed in groups of 3 were implanted with radio frequency identification (RFID) tags. Each homecage was placed over a RFID reader baseplate for the automated monitoring of the social and locomotor activity of each individual rat. For all rats, we acquired homecage data for 24 h following administration of both saline and PCP (2.5 mg/kg). Results: PCP resulted in significantly increased distance travelled from 15 to 60 min post injection. Furthermore, PCP significantly enhanced time spent isolated from cage-mates and this asociality lasted from 60 to 105 min post treatment. Conclusions: Unlike conventional assays, in-cage monitoring captures the temporal duration of drug effects on multiple behaviours in the same group of animals. This approach could benefit psychiatric preclinical drug discovery though improved welfare and increased between-laboratory replicability

Identification of Altered Evoked and Non-Evoked Responses in a Heterologous Mouse Model of Endometriosis-Associated Pain

The aim of this study was to develop and refine a heterologous mouse model of endometriosis-associated pain in which non-evoked responses, more relevant to the patient experience, were evaluated. Immunodeficient female mice (N = 24) were each implanted with four endometriotic human lesions (N = 12) or control tissue fat (N = 12) on the abdominal wall using tissue glue. Evoked pain responses were measured biweekly using von Frey filaments. Non-evoked responses were recorded weekly for 8 weeks using a home cage analysis (HCA). Endpoints were distance traveled, social proximity, time spent in the center vs. outer areas of the cage, drinking, and climbing. Significant differences between groups for von Frey response, climbing, and drinking were detected on days 14, 21, and 35 post implanting surgery, respectively, and sustained for the duration of the experiment. In conclusion, a heterologous mouse model of endometriosis-associated evoked a non-evoked pain was developed to improve the relevance of preclinical models to patient experience as a platform for drug testing

MEK Inhibitors Reverse cAMP-Mediated Anxiety in Zebrafish

SummaryAltered phosphodiesterase (PDE)-cyclic AMP (cAMP) activity is frequently associated with anxiety disorders, but current therapies act by reducing neuronal excitability rather than targeting PDE-cAMP-mediated signaling pathways. Here, we report the novel repositioning of anti-cancer MEK inhibitors as anxiolytics in a zebrafish model of anxiety-like behaviors. PDE inhibitors or activators of adenylate cyclase cause behaviors consistent with anxiety in larvae and adult zebrafish. Small-molecule screening identifies MEK inhibitors as potent suppressors of cAMP anxiety behaviors in both larvae and adult zebrafish, while causing no anxiolytic behavioral effects on their own. The mechanism underlying cAMP-induced anxiety is via crosstalk to activation of the RAS-MAPK signaling pathway. We propose that targeting crosstalk signaling pathways can be an effective strategy for mental health disorders, and advance the repositioning of MEK inhibitors as behavior stabilizers in the context of increased cAMP

Automated recording of home cage activity and temperature of individual rats housed in social groups: The Rodent Big Brother project

Measuring the activity and temperature of rats is commonly required in biomedical research. Conventional approaches necessitate single housing, which affects their behavior and wellbeing. We have used a subcutaneous radiofrequency identification (RFID) transponder to measure ambulatory activity and temperature of individual rats when group-housed in conventional, rack-mounted home cages. The transponder location and temperature is detected by a matrix of antennae in a baseplate under the cage. An infrared high-definition camera acquires side-view video of the cage and also enables automated detection of vertical activity. Validation studies showed that baseplate-derived ambulatory activity correlated well with manual tracking and with side-view whole-cage video pixel movement. This technology enables individual behavioral and temperature data to be acquired continuously from group-housed rats in their familiar, home cage environment. We demonstrate its ability to reliably detect naturally occurring behavioral effects, extending beyond the capabilities of routine observational tests and conventional monitoring equipment. It has numerous potential applications including safety pharmacology, toxicology, circadian biology, disease models and drug discovery